Jobs at Cogniify

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Lead end-to-end client AI transformation engagements: discover and frame executive problems, design and build production-credible PoAs (data pipelines, integrations, LLM/RAG apps) within 2–3 weeks, present to executives, hand off to delivery, mentor junior FDEs, and convert PoAs into full engagements.

Build and maintain Java backend services, REST APIs, and full-stack web application features. Develop frontend functionality with modern JavaScript frameworks, use Python for supporting work, and leverage AI coding tools such as Claude Code. Write tests, debug issues, support deployment and maintenance, and collaborate on design decisions, code reviews, and application quality improvements.

Build and deploy generative AI applications and multi-agent systems on Google Cloud using Vertex AI, Python, BigQuery, RAG, and secure enterprise data integrations. Develop data pipelines, train and serve models, evaluate AI output, protect sensitive information, troubleshoot production systems, and collaborate with technical and business stakeholders.

Build and maintain Java backend services, REST APIs, and full-stack web application features. Develop frontend functionality, use Python for supporting work, and apply AI coding tools such as Claude Code for development, testing, and debugging. Collaborate on design decisions and code reviews, troubleshoot issues, and support application deployment and maintenance across cloud environments.

Build and maintain Java and Spring Boot backend services, REST APIs, SQL integrations, and React or Angular interfaces. Write tested code, troubleshoot issues, improve performance, and support deployment through CI/CD. The role also involves integrating basic AI capabilities such as LLM APIs and AI assistants into application workflows.

Build and maintain production ETL/ELT pipelines, data models, and curated datasets for analytics and AI applications. Ingest data from databases, APIs, SaaS tools, and event streams; implement quality checks, monitoring, documentation, governance, and access controls. Collaborate with analysts, data scientists, and ML engineers on reporting, feature datasets, AI search, and RAG applications. Optimize query performance and pipeline costs while using Git, code reviews, and CI/CD for safe releases.